Parallel incident processing

ABSTRACT

Methods, apparatuses, and computer program products for parallel incident processing are provided. Embodiments include an incident analyzer identifying a pool of incidents and distributing the incidents across a plurality of threads of the incident analyzer. One or more threads of the plurality of threads of the incident analyzer generate a tuple indicating a rule identification and a rule state. The incident analyzer also identifies from the generated tuples, tuples that have the same rule identification and generates a merged tuple by merging the rule state of each of the identified tuples that have the same rule identification.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically, methods, apparatuses, and computer program products for parallel incident processing in a distributed processing system.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited as the beginning of the computer era. Since that time, computer systems have evolved into extremely complicated devices. Today's computers are much more sophisticated than early systems such as the EDVAC. Computer systems typically include a combination of hardware and software components, application programs, operating systems, processors, buses, memory, input/output devices, and so on. As advances in semiconductor processing and computer architecture push the performance of the computer higher and higher, more sophisticated computer software has evolved to take advantage of the higher performance of the hardware, resulting in computer systems today that are much more powerful than just a few years ago.

Modern distributed processing systems for intensive computing may have millions of devices with many processes running on each device all of which are capable of error and status reporting for automated error recovery, reporting to a systems administrator, and for other reasons. In many cases, in the case of an error for example, the sheer number of such error reports and status reports are so overwhelming that they cannot be handled in a meaningful manner. For example, a systems administrator receiving a hundred thousand error reports may be overwhelmed by the sheer number of such reports and therefore in the aggregate those reports become more and more unhelpful and irrelevant.

SUMMARY OF THE INVENTION

Methods, apparatuses, and computer program products for parallel incident processing are provided. Embodiments include an incident analyzer identifying a pool of incidents and distributing the incidents across a plurality of threads of the incident analyzer. One or more threads of the plurality of threads of the incident analyzer generate a tuple indicating a rule identification and a rule state. The incident analyzer also identifies from the generated tuples, tuples that have the same rule identification and generates a merged tuple by merging the rule state of each of the identified tuples that have the same rule identification.

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for parallel incident processing in a distributed processing system according to embodiments of the present invention.

FIG. 2 sets forth a block diagram of automated computing machinery comprising an exemplary computer useful in parallel incident processing in a distributed processing system according to embodiments of the present invention.

FIG. 3 sets forth a block diagram of an exemplary system for parallel incident processing in a distributed processing system in a distributed processing system according to embodiments of the present invention.

FIG. 4 sets forth a diagram illustrating assigning events to an events pool according to embodiments of the present invention.

FIG. 5 sets forth a diagram illustrating assigning alerts to an alerts pool according to embodiments of the present invention.

FIG. 6 sets forth a flow chart illustrating an example method of parallel incident processing in a distributed processing system according to embodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating an additional method of parallel incident processing in a distributed processing system according to embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating an additional method of parallel incident processing in a distributed processing system according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatuses, and computer program products for parallel incident processing in a distributed processing system according to embodiments of the present invention are described with reference to the accompanying drawings, beginning with FIG. 1. FIG. 1 illustrates an exemplary system for parallel incident processing in a distributed processing system (101) according to embodiments of the present invention. A distributed processing system is typically implemented as multiple autonomous or semi-autonomous computers that communicate through a computer network. In such example distributed processing systems, the computers often interact with each other in order to achieve a common goal. A computer program that runs in such an example distributed system is typically called a distributed program, and distributed programming is often used to describe the process of writing such programs.

In the example of FIG. 1, the distributed processing system (101) is implemented as a parallel computer (100), non-volatile memory for the computer in the form of data storage device (118), an output device for the computer in the form of a printer (120), and an input/output device for the computer in the form of a computer terminal (122). The parallel computer (100) in the example of FIG. 1 includes a plurality of compute nodes (102). Each compute node is an automated computing device composed of one or more computer processors, its own computer memory, and its own input/output functionality. The compute nodes (102) are coupled for data communications by several independent data communications networks including a high speed Ethernet network (174), a Joint Test Action Group (‘JTAG’) network (104), a collective or tree network (106) which is optimized for collective operations, and a torus network (108) which is optimized for point to point operations. The tree network (106) is a data communications network that includes data communications links connected to the compute nodes so as to organize the compute nodes as a tree. Each data communications network is implemented with data communications links among the compute nodes (102). The data communications links provide data communications for parallel operations among the compute nodes of the parallel computer (100).

In addition to the compute nodes (102), the parallel computer (100) includes input/output (‘I/O’) nodes (110, 114) coupled to the compute nodes (102) through the high speed Ethernet network (174). The I/O nodes (110, 114) provide I/O services between the compute nodes (102) and I/O devices, which in this example is the data storage device (118), the printer (120) and the terminal (122). The I/O nodes (110, 114) are connected for data communications through a local area network (IAN′) (130). The parallel computer (100) also includes a service node (116) coupled to the compute nodes (102) through the JTAG network (104). The service node (116) provides service common to the compute nodes (102), such as loading programs into the compute nodes (102), starting program execution on the compute nodes (102), retrieving results of program operations on the compute nodes (102), and so on. The service node (116) runs an event and alert analysis module (124) and communicates with a system administrator (128) through a service application interface (126) that runs on the computer terminal (122).

Many of the components of the distributed processing system of FIG. 1, that is the devices of the distributed processing system or processes running on the devices of the distributed processing system of FIG. 1, are capable of some form of error or status reporting through events and many of such components are also capable of receiving alerts in response to one or more of such events. Often in distributed processing systems hundreds of thousands or millions of components may provide or receive incidents, often in the form of events or alerts.

An incident is a generic term used in this specification to mean an identification or notification of a particular occurrence on a component of a distributed processing system such as events described below, a refined identification of an occurrence often based on events such as an alert described below, or other notifications as will occur to those of skill in the art.

Incidents are administered in pools for event and alert analysis according to embodiments of the present invention. A pool of incidents is a collection of incidents organized by the time of either their occurrence, by the time they are logged in an incident queue, included in the pool, or other time as will occur to those of skill in the art. Such incident pools often provide the ability to analyze a group of time related incidents. Often such incident pools are useful in identifying fewer and more relevant incidents in dependence upon multiple related incidents.

An event according to embodiments of the present invention is a notification of a particular occurrence in or on a component of the distributed processing system. Such events are sent from the component upon which the occurrence occurred or another reporting component to an event and alert analysis module according to the present invention. Often events are notifications of errors occurring in a component of the data processing system. Events are often implemented as messages either sent through a data communications network or shared memory. Typical events for event and alert analysis according to embodiments of the present invention include attributes such as an occurred time, a logged time, an event type, an event ID, a reporting component, and a source component, and other attributes.

An alert according to embodiments of the present invention is a refined identification of an occurrence, such as an error, based upon more than one event and therefore provides an identification of the occurrence in the context of its operation in the distributed processing system. Often an alert may be a notification of a particular error type of occurrence that is identified in dependence upon the plurality of events received from one or more components of the data processing system, such as, for example, a link failure among a plurality of devices each of which are producing many events based upon the single link failure, or a power failure provoking thousands of events, and so on.

Alerts are often implemented as messages to be sent through a data communications network or shared memory. Typical alerts according to embodiments of the present invention have attributes attached to them based upon the attributes of the events received from which they are identified.

The event and alert analysis module (124) includes at least two incident analyzers implemented as an event analyzer and an alert analyzer capable of parallel incident processing in a distributed processing system according to embodiments of the present invention. The event and alert analysis module (124) is also implemented as a monitor and checkpoint manager for managing the checkpoints from the incident analyzers.

Specifically, the event and alert analysis module (124) is implemented as automated computing machinery where at least one incident analyzer is configured to identify a pool of incidents and distribute the incidents across a plurality of threads of the incident analyzer. The at least one incident analyzer is also configured to generate a tuple and identify from the generated tuples, tuples that have the same rule identification. In the example of FIG. 1, the at least one incident analyzer is also configured to generate a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.

The arrangement of nodes, networks, and I/O devices making up the exemplary distributed processing system illustrated in FIG. 1 are for explanation only, not for limitation of the present invention. Distributed data processing systems configured to perform parallel incident processing according to embodiments of the present invention may include additional nodes, networks, devices, and architectures, not shown in FIG. 1, as will occur to those of skill in the art. The parallel computer (100) in the example of FIG. 1 includes sixteen compute nodes (102); parallel computers configured to perform parallel incident processing according to embodiments of the present invention sometimes include thousands of compute nodes. In addition to Ethernet, JTAG, collective, and point to point, networks in such data processing systems may support many data communications protocols including for example TCP (Transmission Control Protocol), IP (Internet Protocol), and others as will occur to those of skill in the art. Various embodiments of the present invention may be implemented on a variety of hardware platforms in addition to those illustrated in FIG. 1.

Parallel incident processing in a distributed processing system in accordance with the present invention is generally implemented with computers, that is, with automated computing machinery. In the system of FIG. 1, for example, all the service nodes, I/O nodes, compute nodes, of the parallel computer are implemented to some extent at least as computers. For further explanation, therefore, FIG. 2 sets forth a block diagram of automated computing machinery comprising an exemplary computer (252) useful in performing parallel incident processing according to embodiments of the present invention. The computer (252) of FIG. 2 includes at least one computer processor (256) or ‘CPU’ as well as random access memory (268) (RAM′) which is connected through a high speed memory bus (266) and bus adapter (258) to processor (256) and to other components of the computer (252) and through an expansion bus to adapters for communications with other components of a distributed processing system (101).

Stored in RAM (268) is an event and alert analysis module (124), a module of automated computing machinery for performing parallel incident processing according to embodiments of the present invention. The event and alert analysis module (124) includes two incident analyzers, a monitor (204), and a checkpoint manager (299) according to embodiments of the present invention.

The checkpoint manager (299) performs parallel incident processing according to embodiments of the present invention by processing checkpoints from the incident analyzers. The monitor (204) is configured to perform parallel incident processing in a distributed processing system according to embodiments of the present invention. The monitor (204) performs parallel incident processing according to embodiments of the present invention by sending to the plurality of incident analyzers, streams of incidents.

The incident analyzers include an event analyzer (208) and an alert analyzer (218). The event analyzer of FIG. 2 is a module of automated computing machinery capable of identifying alerts in dependence upon received events. That is, event analyzers typically receive events and produce alerts. In many embodiments, a plurality of event analyzers are implemented in parallel. Often such event analyzers are assigned to a particular pool of events and may be focused on events from a particular component or caused by a particular occurrence to produce a more concise set of alerts.

The alert analyzer (218) of FIG. 2 is a module of automated computing machinery capable of identifying alerts for transmission from events and other alerts, identifying additional alerts for transmission, and suppressing unnecessary, irrelevant, or otherwise unwanted alerts identified by the event analyzer. That is, alert analyzers typically receive alerts and events and produce or forward alerts in dependence upon those alerts and events. In many embodiments, a plurality of alert analyzers are implemented in parallel. Often such alert analyzers are assigned to a particular pool of alerts and may be focused on alerts with particular attributes to produce a more concise set of alerts.

In addition to the general functions described above, the event analyzer (208) and the alert analyzer (218) may each be configured to perform parallel incident processing in a distributed processing system according to embodiments of the present invention. An event analyzer performs parallel incident processing according to embodiments of the present invention by identifying a pool of events and distributing the events across a plurality of threads of the event analyzer. The event analyzer also generates a tuple and identifies from the generated tuples, tuples that have the same rule identification. In the example of FIG. 2, the event analyzer also generates a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.

An alert analyzer performs parallel incident processing according to embodiments of the present invention by identifying a pool of incidents and distributing the incidents across a plurality of threads of the alert analyzer. The alert analyzer also generates a tuple and identifies from the generated tuples, tuples that have the same rule identification. In the example of FIG. 2, the alert analyzer also generates a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.

Also stored in RAM (268) is an operating system (254). Operating systems useful for relevant alert delivery according to embodiments of the present invention include UNIX™ Linux™ Microsoft XP™ AIX™ IBM's i5/OS™ and others as will occur to those of skill in the art. The operating system (254), event and alert analysis module (124), the event analyzer (208), the alert analyzer (218) in the example of FIG. 2 are shown in RAM (268), but many components of such software typically are stored in non-volatile memory also, such as, for example, on a disk drive (270).

The computer (252) of FIG. 2 includes disk drive adapter (272) coupled through expansion bus (260) and bus adapter (258) to processor (256) and other components of the computer (252). The disk drive adapter (272) connects non-volatile data storage to the computer (252) in the form of disk drive (270). Disk drive adapters useful in computers for parallel incident processing according to embodiments of the present invention include Integrated Drive Electronics (‘IDE’) adapters, Small Computer System Interface (SCSI′) adapters, and others as will occur to those of skill in the art. Non-volatile computer memory also may be implemented for as an optical disk drive, electrically erasable programmable read-only memory (so-called ‘EEPROM’ or ‘Flash’ memory), RAM drives, and so on, as will occur to those of skill in the art.

The example computer (252) of FIG. 2 includes one or more input/output (′I/O′) adapters (278). I/O adapters implement user-oriented input/output through, for example, software drivers and computer hardware for controlling output to display devices such as computer display screens, as well as user input from user input devices (281) such as keyboards and mice. The example computer (252) of FIG. 2 includes a video adapter (209), which is an example of an I/O adapter specially designed for graphic output to a display device (280) such as a display screen or computer monitor. The video adapter (209) is connected to processor (256) through a high speed video bus (264), bus adapter (258), and the front side bus (262), which is also a high speed bus.

The exemplary computer (252) of FIG. 2 includes a communications adapter (267) for data communications with other computers (282) and for data communications with a data communications network (200). Such data communications may be carried out serially through RS-232 connections, through external buses such as a Universal Serial Bus (USW), through data communications data communications networks such as IP data communications networks, and in other ways as will occur to those of skill in the art. Communications adapters implement the hardware level of data communications through which one computer sends data communications to another computer, directly or through a data communications network. Examples of communications adapters useful for parallel incident processing according to embodiments of the present invention include modems for wired dial-up communications, Ethernet (IEEE 802.3) adapters for wired data communications network communications, and 802.11 adapters for wireless data communications network communications.

For further explanation, FIG. 3 sets forth a block diagram of an exemplary system for parallel incident processing and relevant alert delivery in a distributed processing system (102) according to embodiments of the present invention. The system of FIG. 3 includes an event and alert analysis module (124). The event and alert analysis module (124) of FIG. 3 receives in an event queue (306) a plurality of events (302) from one or more components of a distributed processing system (102). A component of a distributed processing system according to embodiments of the present invention may be a device of the distributed processing system or a process running on a device of the distributed processing. Such components are often capable of some form of event transmission, often for error or status reporting.

An event according to embodiments of the present invention is a notification of a particular occurrence in or on a component of the distributed processing system. Such events are sent from the component upon which the occurrence occurred or another reporting component to an event and alert analysis module according to the present invention. Often events are notifications of errors occurring in a component of the data processing system. Events are often implemented as messages either sent through a data communications network or shared memory. Typical events for event and alert analysis according to embodiments of the present invention include attributes such as an occurred time, a logged time, an event type, an event ID, a reporting component, and a source component, and other attributes. An occurred time is the time at which the event occurred on the component. A logged time is the time the event was included in the event queue (306) and is typically inserted into the event by a monitor. An event type is a generic type of event such as for example, power error, link failure error, errors related to not receiving messages or dropping packets and so on as will occur to those of skill in the art. An event ID is a unique identification of the event. A reporting component is an identification of the component that reported the event. A source component is an identification of the component upon which the event occurred. In many cases, but not all, the reporting component and source component are the same component of the distributed processing system.

The event and analysis module (124) of FIG. 3 also includes a checkpoint manager (299) that is configured to perform parallel incident processing in a distributed processing system according to embodiments of the present invention. The checkpoint manager (299) performs parallel incident processing according to embodiments of the present invention by receiving from each incident analyzer of a plurality of incident analyzers, a checkpoint indicating an incident having the oldest identification number still in analysis by the incident analyzer at the time associated with the checkpoint. The checkpoint manager (299) is also configured to examine each received checkpoint by the checkpoint manager, to identify, as a restore incident, an incident having the oldest identification number indicated in any of the received checkpoints.

In the example of FIG. 3, the monitor (204) receives events from components of the distributed processing system and puts the received events (302) in the event queue (306). The monitor (204) of FIG. 3 may receive events from components of the distributed processing system on their motion, may periodically poll one or more of the components of the distributed processing system, or receive events from components in other ways as will occur to those of skill in the art.

In addition, the monitor (204) is configured to perform parallel incident processing in a distributed processing system according to embodiments of the present invention. The monitor (204) performs parallel incident processing according to embodiments of the present invention by sending to the plurality of incident analyzers, a stream of incidents beginning with the identified restore incident and continuing with any incidents having a newer identification number than the identified restore incident.

The system of FIG. 3 also includes an event analyzer (208). The event analyzer (208) of FIG. 3 is a module of automated computing machinery configured to identify alerts in dependence upon received events. That is, event analyzers typically receive events and produce alerts. In many embodiments, a plurality of event analyzers are implemented in parallel. Often event analyzers are assigned to a particular pool of events and may be focused on events from a particular component or caused by a particular occurrence to produce a more concise set of alerts.

The event analyzer (208) of FIG. 3 assigns each received event (302) to an events pool (312). An events pool (312) is a collection of events organized by the time of either their occurrence, by the time they are logged in the event queue, included in the events pool, or other time as will occur to those of skill in the art. That is, events pools are a collection of events organized by time. Such events pools often provide the ability to analyze a group of time related events to identify alerts in dependence upon them. Often such events pools are useful in identifying fewer and more relevant alerts in dependence upon multiple related events.

In addition, the event analyzer (208) is also configured to perform parallel incident processing according to embodiments of the present invention. An event analyzer performs parallel incident processing according to embodiments of the present invention by identifying an event having the oldest identification number still in analysis by the event analyzer at a particular time and sending to the checkpoint manager, a checkpoint identifying the identified event, the checkpoint associated with the particular time. An event analyzer performs parallel incident processing according to embodiments of the present invention by processing from the stream of events only the event indicated in the last checkpoint of the event analyzer and any subsequent events having a newer identification number than the indicated event.

As mentioned above, in some embodiments of the present invention, more than one event analyzer may operate in parallel. As such, each event analyzer may maintain one or more events pools for performing parallel incident processing according to embodiments of the present invention. Assigning by the event analyzer the events to an events pool may therefore include selecting only events from one or more particular components. In such embodiments, particular components may be selected for a particular events pool to provide events associated with a particular period of time from a particular set of one or more components.

Assigning by the event analyzer the events to an events pool may also be carried out by selecting only events of a particular event type. In such embodiments, particular events may be selected for a particular events pool to provide events associated with a particular period of time from a particular set of event types. The event analyzer (208) in the example of FIG. 3 identifies in dependence upon the event analysis rules (310) and the events assigned to the events pool one or more alerts (314). Event analyses rules (310) are a collection of predetermined rules for meaningfully parsing received events to identify relevant alerts in dependence upon the events.

The event analysis rules (310) of FIG. 3 include event arrival rules (330), events pool operation rules (332), event suppression rules (334), and events pool closure rules (336). The event arrival rules (330) are configurable predetermined rules for identifying alerts in dependence upon events in real time as those events are assigned to the events pool. That is, the event arrival rules (330) identify alerts in dependence upon events before closing the events pool. Such rules are typically predetermined to identify particular alerts in dependence upon attributes of those events. Event arrival rules may for example dictate identifying a particular predetermined alert for transmission to a systems administrator in dependence upon a particular event type or component type for the event or other attribute of that event. Such rules are flexible and may be tailored to a particular distributed computing system and its functions.

An alert according to embodiments of the present invention is refined identification of an occurrence, such as an error based upon more than one event, and therefore provides an identification of the occurrence in the context of its operation in the distributed processing system. Often an alert may be a notification of a particular error type of occurrence that is identified in dependence upon the plurality of events received from one or more components of the data processing system, such as, for example, a link failure among a plurality of devices each of which are producing many events based upon the single link failure, or a power failure provoking thousands of events, and so on.

Alerts are often implemented as messages to be sent through a data communications network or shared memory. Typical alerts according to embodiments of the present invention have attributes attached to them based upon the attributes of the events received from which they are identified.

The events pool operation rules (332) are configurable predetermined rules for controlling the operations of the events pool. Such rules includes rules identifying the initial predetermined period of time for each events pool, rules dictating the length of time extended to the pool upon the assignment of each new event to the pool, rules dictating the minimum time an event must be in a pool before that event is included in a collection of events when the pool is closed, rules dictating the amount to extend the initial predetermined period of time based on an arrival rate of events assigned to an events pool, rules governing the closing of an events pool, and others as will occur to those of skill in the art. Such rules are flexible and may be tailored to a particular distributed computing system and its functions.

The event suppression rules (334) are configurable predetermined rules for suppressing one or more events in a closed pool of events used in identifying alerts. That is, often events in the closed events pool may be duplicate events, redundant events, or otherwise unnecessary or unhelpful events in identifying alerts. Such suppression rules are typically predetermined to delete, drop, or otherwise ignore those suppressed events. Event suppression rules may for example dictate that more than a threshold number of events of a particular event type or component type are to be suppressed. Such rules are also flexible and may be tailored to a particular distributed computing system and its functions.

The events pool closure rules (336) are configurable predetermined rules for identifying alerts in dependence upon unsuppressed events in the closed events pool and alerts identified by the event arrival rules. That is, events pool closure rules identify new alerts in dependence upon one or more or even all of the unsuppressed events in the closed events pool. The events pool closure rules also identify alerts in dependence upon the alerts identified by the event arrival rules (330) or a combination of the alerts identified by the event arrival rules (330) and one or more of the unsuppressed events in the closed events pool.

The event analyzer (208) in the example of FIG. 3 sends all the alerts (314) identified by the event analyzer (208) to an alert analyzer (218). The alert analyzer of FIG. 3 is a module of automated computing machinery capable of identifying alerts for transmission from events and other alerts, identifying additional alerts for transmission, and suppressing unnecessary, irrelevant, or otherwise unwanted or unhelpful alerts identified by the event analyzer. That is, alert analyzers typically receive alerts and events and produce or forward alerts in dependence upon those alerts and events. In many embodiments, a plurality of alert analyzers are implemented in parallel. The alerts (316) in the example of FIG. 3 are sent from the event analyzer (208) to the alert analyzer (218) through an alerts queue (316).

The alert analyzer (218) of FIG. 3 assigns each of the identified alerts (314) to an alerts pool (324). An alerts pool is a collection of alerts organized by the time of one or more the events causing the alert to be identified, the time the alert is identified, or other time as will occur to those of skill in the art. That is, alerts pools are a collection of alerts organized by time. Such alerts pools often provide the ability to analyze a groups alerts identified and included in the alerts pool according to some time. Often such alerts pools are useful in identifying fewer and more relevant alerts in dependence upon multiple related events and multiple related alerts.

The alert analyzer (218) of FIG. 3 determines in dependence upon alert analysis rules (322) and the alerts in the alerts pool whether to suppress any alerts. Suppressing an alert is typically carried out by dropping the alert, deleting the alert or otherwise ignoring or not transmitting the suppressed alert to a component of the distributed processing system.

The alert analysis rules (322) are a collection of rules for suppressing one or more alerts to provide a more relevant set of alerts for transmission to a component of the distributed processing system, such as for example, for display to a systems administrator and to identify additional alerts for transmission to one or more components of the distributed processing system. Alert analysis rules for example may dictate that duplicate alerts are to be suppressed, alerts of a particular type for transmission to a particular component are to be suppressed, alerts of a particular type be transmitted to a particular component are to be suppressed and so on as will occur to those of skill in the art. Such alerts may be more meaningful to a component of the distributed processing system for automated error recovery or for a systems administrator who may otherwise be less informed by a number of raw unanalyzed alerts.

The alert analyzer (218) of FIG. 3 also has access to the events queue (306). The alert analyzer (218) of FIG. 3 in dependence upon the alert analysis rules may, in some embodiments select events from the events queue and determine whether to suppress any alerts in dependence upon the selected events. That is, alert analysis rules may also take into account events and their attributes for suppressing alerts and for identifying additional alerts for transmission to one or more components. Such events may be related to the alerts in the alerts pool or independent from such alerts.

The alert analyzer (218) of FIG. 3 transmits the unsuppressed alerts to one or more components of the distributed processing system. The alert analyzer may transmit the unsuppressed alerts to one or more components of the distributed processing system by sending the alert as a message across a data communications network, through shared memory, or in other ways as will occur to those of skill in the art. In the example of FIG. 3, the unsuppressed alerts (320) are transmitted to the terminal (122) for display to the systems administrator (128).

The alert analyzer (218) of FIG. 3 is also configured to identify in dependence upon alert analysis rules (322), the alerts in the alerts pool (324), and selected events (306) one or more additional alerts and transmitting the one or more components of the distributed processing system. The additional alerts may include one or more alerts not identified by the event analyzer. Such additional alerts may provide additional information to a component of the distributed processing system of a systems administrator.

In addition, the alert analyzer (218) is also configured to perform parallel incident processing according to embodiments of the present invention. An alert analyzer performs parallel incident processing according to embodiments of the present invention by identifying an alert having the oldest identification number still in analysis by the alert analyzer at a particular time and sending to the checkpoint manager, a checkpoint identifying the identified alert, the checkpoint associated with the particular time. An alert analyzer performs parallel incident processing according to embodiments of the present invention by processing from a the stream of alerts only the alert indicated in the last checkpoint of the alert analyzer and any subsequent alerts having a newer identification number than the indicated alert.

As mentioned above, parallel incident processing according to embodiments of the present invention may include assigning events to an events pool and those pools are administered according to embodiments of the present invention. For further explanation, FIG. 4 sets forth a diagram illustrating assigning events to an events pool according to embodiments of the present invention. An events pool (312) is a collection of events organized by the time of either their occurrence, by the time they are logged in the event queue, included in the events pool, or other time as will occur to those of skill in the art. That is, events pools are a collection of events organized by time. Such events pools often provide the ability to analyze a group of time related events and to identify alerts in dependence upon them. Often such events pools are useful in identifying fewer and more relevant alerts in dependence upon multiple related events.

Events pools according to embodiments of the present invention are typically operated according to events pool operation rules which are themselves often included in event analysis rules. Such events pool operation rules are configurable predetermined rules for controlling the operations of the events pool. Such rules includes rules identifying the initial predetermined period of time for each events pool, rules dictating the length of time extended to the pool upon the assignment of each new event to the pool, rules dictating the minimum time an event must be in a pool before that event is included in a collection of events when the pool is closed, rules dictating the amount to extend the initial predetermined period of time based on an arrival rate of events assigned to an events pool, rules governing the closing of an events pool, and others as will occur to those of skill in the art. Such rules are flexible and may be tailored to a particular distributed computing system and its functions.

Events are often assigned to an events pool according to their logged time. That is, events are typically inserted into the events pool in the order that they are received in the event queue. In the example of FIG. 4, the timing of the events pool (312) is initiated when the first event ‘Event 0’ (400) is assigned to the events pool (312) at time t₀. The events pool of FIG. 4 is initiated for a predetermined initial period of time from t₁ to t_(f). That is, upon receiving the first event ‘Event 0’ (400) the events pool of FIG. 4 has a predetermined initial period of time beginning at t₁ and ending at t_(f). The predetermined initial period of time may be configured in dependence upon a number of factors as will occur to those of skill in the art such as, the number of components in the distributed processing system, the frequency of receiving events, the types of events typically received and so on as will occur to those of skill in the art.

In the example FIG. 4, the initial period of time is extended for each new event assigned to the events pool during the predetermined initial period from t₁ to t_(f) by a particular period of time assigned to the event. In the example of FIG. 4 upon assigning ‘Event 1’ (402) to the events pool (312) the predetermined initial period of time t₀−t_(f) is extended by ‘Extension 1’ (406) having a time of e1 thereby creating a new time for closing the events pool (312) at t_(f+e1) if no other events are assigned to the pool before t_(f+e1). Similarly, in the example of FIG. 4 upon assigning ‘Event 2’ (404) to the events pool having a time of e2, the now extended period of time from t₀-t_(f+e1) is extended again by extension 2 (406) thereby establishing a new time for closing the pool at time t_(f+e1+e2) if no other events are assigned to the pool before t_(f+e1+e2) or before some maximum time for the events pool has expired. In this manner, the events pool is extended with each received event until a collection of events that may be usefully used to identify alerts is assigned to the events pool. According to embodiments of the present invention, the predetermined initial period of time may be extended based on an arrival rate at which events are assigned to an events pool.

In typical embodiments of the present invention, events pools may have a maximum duration that can no longer be extended. In such cases, a requirement may exist that an event that has not resided in the events pool for a threshold period of time be moved to a next events pool. In some embodiments, the attributes of such an event that is moved to the next events pool are used for relevant alert delivery with the initial events pool and in other embodiments; the attributes of such an event are used for relevant alert delivery with the next events pool to which that event is moved.

In the example of FIG. 4, when conditions are met to close the pool an events analyzer determines for each event (400, 402, 404) in the events pool (312) whether the event has been in the pool for its predetermined minimum time for inclusion in a pool. If the event has been in the pool for its predetermined minimum time, the event is included in the closed pool for event analysis for relevant alert delivery. If the event has not been in the pool for its predetermined minimum time, the event is evicted from the closed pool and included a next pool for event analysis for relevant alert delivery.

In many embodiments, a plurality of events pools may be used in parallel and one or more of such events pools are assigned to a particular events analyzer. In such embodiments, events analyzers may be directed to events in events pools having particular attributes.

As mentioned above, parallel incident processing according to embodiments of the present invention may include assigning alerts to an alerts pool and those pools are administered according to embodiments of the present invention. For further explanation, FIG. 5 sets forth a diagram illustrating assigning alerts to an alerts pool according to embodiments of the present invention. The alerts pool (224) of FIG. 5 operates in a manner similar to the events pool of FIG. 4. That is, the alerts pool according to the example of FIG. 5 includes alerts and the timing of the alerts pool begins with the first alert ‘Alert 0’ (500) at time t₀ and is configured to have a predetermined initial period of time t₀−tf. In the example of FIG. 5, the initial period of time is extended for each new alert assigned to the alerts pool in the predetermined initial period from t₁ to t_(f) by a particular period of time assigned to the alert. In the example of FIG. 5, upon assigning ‘Alert 1’ (502) to the alerts pool (324) the predetermined initial period of time t₀−t_(f) is extended by ‘Extension 1’ (506) having a time of e1 thereby creating a new time for closing the alerts pool (324) at t_(f+e1) if no other alerts are assigned to the pool before t_(f+e1). Similarly, in the example of FIG. 5 upon assigning ‘Alert 2’ (504) to the alerts pool having a time of e2, the now extended period of time from t₀−t_(f+e1) is extended again by ‘Extension 2’ (506) thereby establishing a new time for closing the pool at time t_(f+e1+e2) if no other alerts are assigned to the pool before t_(f+e1+e2) or before some maximum time for the alerts pool has expired. According to embodiments of the present invention, the predetermined initial period of time may be extended based on an arrival rate at which alerts are assigned to an alerts pool.

In typical embodiments of the present invention, alerts pools may have a maximum duration that can no longer be extended. In such cases, a requirement may exist that an alert that has not resided in the alerts pool for a threshold period of time be moved to a next alerts pool. In some embodiments, the attributes of such an alert that is moved to the next alerts pool are used for relevant alert delivery according to embodiments of the present invention with the initial alerts pool and in other embodiments, the attributes of such an alert are used for relevant alert delivery with the next alerts pool to which that alert is moved.

In the example of FIG. 5, when conditions are met to close the pool an alerts analyzer determines for each alert (500, 502, 504) in the pool (324) whether the alert has been in the pool for its predetermined minimum time for inclusion in a pool. If the alert has been in the pool for its predetermined minimum time, the alert is included in the closed pool for alert analysis for relevant alert delivery according to embodiments of the present invention. If the alert has not been in the pool for its predetermined minimum time, the alert is evicted from the closed pool and included a next pool for alert analysis for relevant alert delivery according to embodiments of the present invention.

In many embodiments, a plurality of alerts pools may be used in parallel and one or more of such alerts pools are assigned to a particular alerts analyzer. In such embodiments, alerts analyzers may be directed to alerts in alerts pools having particular attributes.

As mentioned above, parallel incident processing according to embodiments of the present invention may include the administration of one or more pools of incidents such as events, alerts or other incidents as will occur to those of skill in the art. For further explanation, FIG. 6 sets forth a flow chart illustrating an example method of performing parallel incident processing for incident analysis in a distributed processing system in a distributed processing system according to embodiments of the present invention. The method of FIG. 6 includes receiving (602) in an event queue a plurality of events (302) from one or more components of a distributed processing system. Attributes of events useful in performing parallel incident processing for incident analysis in a distributed processing system according to embodiments of the present invention may include an occurred time, a logged time, an event type, an event ID, a reporting component, and a source component.

Receiving (602) in an event queue a plurality of events (302) from one or more components of a distributed processing system may be carried out by receiving an event initiated by one or more components of the data processing system and storing the event in the event queue according to the time in which the event occurred or according to the time the event was received. Receiving (602) in an event queue a plurality of events (302) from one or more components of a distributed processing system also may be carried out by polling a component for status and receiving in response an event and storing the event in the event queue according to the time in which the event occurred or according to the time the event was received.

The method of FIG. 6 also includes assigning (604) by an event analyzer each received event to an events pool (312). In some embodiments of the present invention, assigning (604) by an event analyzer each received event (302) to an events pool (312) may be carried out by assigning events to the events pool according to the logged time. Assigning (604) by an event analyzer each received event (302) to an events pool (312) may also be carried out in dependence upon attributes of the event. Such attributes may include an identification or type of the component upon which an occurrence occurred to create the event, the reporting component of the event, the event ID, the event type, and so on as will occur to those of skill in the art.

An events pool according to the method of FIG. 6 includes events occurring during a predetermined initial period of time and in the example of FIG. 6 assigning (604) by the event analyzer each received event to an events pool includes extending (626) for each event assigned to the events pool the predetermined initial period of time by a particular period of time assigned to the event.

The event analyzer includes event analysis rules (310) including, event arrival rules, events pool operation rules, event suppression rules, and events pool closure rules. Event arrival rules are configurable predetermined rules for identifying alerts in dependence upon events in real time as those events are assigned to the events pool. That is, event arrival rules identify alerts in dependence upon events before closing the events pool. Such rules are flexible and may be tailored to a particular distributed computing system and its functions.

An alert according to embodiments of the present invention is refined identification of an occurrence, such as an error based upon more than one event, and therefore provides an identification of the occurrence in the context of its operation in the distributed processing system. Often an alert may be a notification of a particular error type of occurrence that is identified in dependence upon the plurality of events received from one or more components of the data processing system, such as, for example, a link failure among a plurality of devices each of which are producing many events based upon the single link failure, or a power failure provoking thousands of events, and so on.

Alerts are often implemented as messages to be sent through a data communications network or shared memory. Typical alerts according to embodiments of the present invention have attributes attached to them based upon the attributes of the events received from which they are identified.

Events pool operation rules are configurable predetermined rules for controlling the operations of the events pool. Such rules includes rules identifying the initial predetermined period of time for each events pool, rules dictating the length of time extended to the pool upon the assignment of each new event to the pool, rules dictating the minimum time an event must be in a pool before that event is included in a collection of events when the pool is closed, rules governing the closing of an events pool, and others as will occur to those of skill in the art. Such rules are flexible and may be tailored to a particular distributed computing system and its functions.

Event suppression rules are configurable predetermined rules for suppressing one or more events in a closed pool of events used in identifying alerts. That is, often events in the closed events pool may be duplicate events, redundant events, or otherwise unnecessary or unhelpful events in identifying alerts. Such suppression rules are typically predetermined to delete, drop, or otherwise ignore those suppressed events. Event suppression rules may for example dictate that more than a threshold number of events of a particular event type or component type are to be suppressed. Such rules are also flexible and may be tailored to a particular distributed computing system and its functions.

Events pool closure rules are configurable predetermined rules for identifying alerts in dependence upon unsuppressed events in the closed events pool and alerts identified by the event arrival rules. That is, events pool closure rules identify new alerts in dependence upon one or more or even all of the unsuppressed events in the closed events pool. The events pool closure rules also identify alerts in dependence upon the alerts identified by the event arrival rules or a combination of the alerts identified by the event arrival rules and one or more of the unsuppressed events in the closed events pool.

The method of FIG. 6 also includes identifying (610) by the event analyzer in dependence upon the event arrival rules and the events assigned to the events pool one or more alerts (314). Identifying (610) by the event analyzer in dependence upon the event arrival rules and the events assigned to the events pool one or more alerts (314) may be carried out by identifying alerts in dependence upon one or more attributes of the events as that event is assigned to the events pool. Identifying (610) by the event analyzer in dependence upon the event arrival rules and the events assigned to the events pool one or more alerts (314) may be carried by comparing the attributes of the events to the event arrival rules and identifying as a result of the comparison one or more alerts. Such attributes may include the type of component from which the event was received, the type of component creating the event, the identification of the component creating the event, the time the event was created or received, an error reported in the event, and many others as will occur to those of skill in the art.

The method of FIG. 6 also includes closing (612), by the event analyzer in dependence upon the events pool operation rules, the events pool (312). Closing (612), by the event analyzer in dependence upon the events pool operation rules, the events pool (312) may be carried out by determining that conditions dictated by the events pool operation rules have been met to stop assigning new events to the events pool and identifying in dependence upon those events pool operation rules the particular events that are included in the closed pool of events.

Closing the events pool may be carried out by determining that the initial period of time for the events pool and any particular periods of time for events received in the events pool extended to the initial period of time have expired. In such cases, if no new events are received prior to the expiration of the initial period of time for the events pool and any particular periods of time for events received in the events pool extended to the initial period of time the pool is closed.

Closing the events pool may also be carried out by determining that a maximum duration for the events pool has expired. In such cases, regardless of the number of new events being received after a maximum duration for the events pool has expired the pool is closed. In such embodiments, a maximum duration for the events pool prevents the events pool from including more events than are useful for relevant alert delivery according to embodiments of the present invention.

The method of FIG. 6 also includes determining (614), by the events analyzer in dependence upon the event suppression rules, whether to suppress one or more events in the closed events pool (312). Determining (614), by the events analyzer in dependence upon the event suppression rules, whether to suppress one or more events in the closed events pool (312) may be carried out by determining in dependence upon the attributes of one or more events in the closed pool whether to delete, drop, or otherwise ignore one or more of the events in the closed pool.

The method of FIG. 6 includes identifying (616) by the event analyzer in dependence upon the events pool closure rules and any unsuppressed events assigned to the events pool, one or more additional alerts (617). Identifying (616) by the event analyzer in dependence upon the events pool closure rules and any unsuppressed events assigned to the events pool, one or more additional alerts (617) may be carried out by identifying alerts in dependence upon one or more attributes of the events as that event is assigned to the events pool. Identifying (616) by the event analyzer in dependence upon the events pool closure rules and any unsuppressed events assigned to the events pool, one or more additional alerts (617) may be carried out by selecting the unsuppressed events for the events pool, comparing the attributes of the unsuppressed events of the events pool to the pool closure rules, and identifying as a result of the comparison one or more additional alerts. Such attributes may include the type of component from which one or more of the unsuppressed events are received, the type of components creating the unsuppressed events, the identification of the component creating the unsuppressed events, the time the events were created or received, one or more errors reported by the events event, the number of events in the pool, and many others as will occur to those of skill in the art.

The method of FIG. 6 includes sending (618) by the event analyzer to an alert analyzer all the alerts identified by the event analyzer. Sending (618) by the event analyzer to an alert analyzer all the alerts (314) identified by the event analyzer may be carried out by sending a message containing the alerts from the event analyzer to the alert analyzer. Such a message may be sent from the event analyzer to the alert analyzer across a network, through shared memory, or in other ways as will occur to those of skill in the art.

The method of FIG. 6 includes assigning (620) by the alert analyzer the identified alerts to an alerts pool (324). An alerts pool according to the method of FIG. 6 has a predetermined initial period of time and in the example of FIG. 6 assigning (620) by the alert analyzer the identified alerts to an alerts pool (324) includes extending for each alert assigned to the alerts pool the predetermined initial period of time by a particular period of time assigned to the alert. Assigning (620) by the alert analyzer the identified alerts to an alerts pool (324) also may be carried out in dependence upon attributes of the alerts. Such attributes may include an identification or type of the component upon which an occurrence occurred to create the event that was used to identify the alert, the alert ID, the alert type, and so on as will occur to those of skill in the art.

The method of FIG. 6 includes determining (622) by the alert analyzer in dependence upon alert analysis rules (322) and the alerts in the alerts pool whether to suppress any alerts. Determining (622) by the alert analyzer in dependence upon alert analysis rules (322) and the alerts in the alerts pool whether to suppress any alerts may be carried out in dependence upon one or more attributes of the alerts. Such attributes may include an identification or type of the component upon which an occurrence occurred to create the event that was used to identify the alert, the alert ID, the alert type, and so on as will occur to those of skill in the art. In such embodiments, determining (622) by the alert analyzer in dependence upon alert analysis rules (322) and the alerts in the alerts pool whether to suppress any alerts may be carried out by comparing the attributes of the alerts in the alerts pool to the alert analysis rules and identifying as a result of the comparison one or more alerts for suppression according to the event analysis rules.

The method of FIG. 6 includes transmitting (620) the unsuppressed alerts to one or more components of the distributed processing system. Transmitting (620) the unsuppressed alerts to one or more components of the distributed processing system may be carried out by sending a message containing the alert to one or more components of the distributed processing system. In many cases, an alert may be sent as a message to a systems administrator advising the systems administrator of one or more occurrences within the distributed processing system.

As mentioned above, alert analysis rules may select additional alerts or suppress alerts in dependence upon events. In such embodiments, determining whether to suppress any alerts includes selecting events and determining whether to suppress any alerts in dependence upon the selected events. The method of FIG. 6 therefore also includes identifying (626) by the alert analyzer in dependence upon alert analysis rules (322), the alerts in the alerts pool (324), and any selected events one or more additional alerts and in the method of FIG. 6, transmitting (628) the unsuppressed alerts also includes transmitting (630) any additional alerts to one or more components of the distributed processing system.

For further explanation, FIG. 7 sets forth a flow chart illustrating an exemplary method of parallel incident processing in a distributed processing system according to embodiments of the present invention. An incident is a generic term used in this specification to mean an identification or notification of a particular occurrence on a component of a distributed processing system such as events described below, a refined identification of an occurrence often based on events such as an alert described below, or other notifications as will occur to those of skill in the art.

As discussed above, examples of incidents include an event and an alert. An event according to embodiments of the present invention is a notification of a particular occurrence in or on a component of the distributed processing system. Such events are sent from the component upon which the occurrence occurred or another reporting component to an event and alert analysis module according to the present invention. Often events are notifications of errors occurring in a component of the data processing system. Events are often implemented as messages either sent through a data communications network or shared memory. Typical events for event and alert analysis according to embodiments of the present invention include attributes such as an occurred time, a logged time, an event type, an event ID, a reporting component, and a source component, and other attributes.

An alert according to embodiments of the present invention is a refined identification of an occurrence, such as an error, based upon more than one event and therefore provides an identification of the occurrence in the context of its operation in the distributed processing system. Often an alert may be a notification of a particular error type of occurrence that is identified in dependence upon the plurality of events received from one or more components of the data processing system, such as, for example, a link failure among a plurality of devices each of which are producing many events based upon the single link failure, or a power failure provoking thousands of events, and so on.

The method of FIG. 7 includes an incident analyzer (700) identifying (702) a pool (750) of incidents (752). A pool of incidents is a collection of incidents organized by the time of either their occurrence, by the time they are logged in an incident queue, included in the pool, or other time as will occur to those of skill in the art. Such incident pools often provide the ability to analyze a group of time related incidents. Often such incident pools are useful in identifying fewer and more relevant incidents in dependence upon multiple related incidents. Identifying (702) a pool (750) of incidents (752) may be carried out by retrieving the incidents (752) assigned to the incident pool (750) associated with incident analyzer (700).

The method of FIG. 7 also includes the incident analyzer (700) distributing (704) the incidents (752) across a plurality of threads (754) of the incident analyzer (700). An incident analyzer may utilize threads to perform event and alert processing. In an event and alert processing system, each incident analyzer may have a plurality of threads for processing events or alerts. Distributing (704) the incidents (752) across a plurality of threads (754) of the incident analyzer (700) may be carried out by assigning each thread of the plurality of threads, a portion of the incidents retrieved from the incident pool (750). By spreading the incidents across the plurality of threads, the incidents may be processed in parallel. As part of processing an incident, a thread applies incident analysis rules (e.g., event analysis rules and alert analysis rules). The threads use these incident analysis rules to analyze the incidents and create a set of tuples. Each tuple has a rule identification which identifies a particular rule type. The rule identification may also include a location identification and even a pool identification if the pools are static, that is, of fixed size. A pool identification allows multiple pools to be processed simultaneously by the same set of threads. A location identification may be used to limit the application of the rule to a particular location.

Each tuple also has a rule state. A rule state is either a collection of incidents, or in some cases, an identification of some form of preprocessing. Such preprocessing may be an indication that one or more conditions of the rule of the rule type have been satisfied. For example, the rule may state that if there is any occurrence of event type A, then a set of events does not need to be stored in the rule state, the rule state may simply indicate its state is true. Alternatively, if a rule includes an incident gathering phase and a rule processing phase, the thread may in the incident gathering phase, identify which of the incidents are applicable to the rule. These identified incidents may then be stored as the rule state of a tuple. That is, the rule state may either be a collection of incidents or represent that at least one incident has satisfied at least a portion of the rule.

The method of FIG. 7 also includes one or more threads of the incident analyzer generating (706) a tuple (756). In the example of FIG. 7, each tuple generated by a thread has a rule identification (770) and a rule state (772). In a particular embodiment, if a thread determines that a particular rule does not have any incidents that are applicable, the thread may not generate a tuple for that particular rule. Generating (706) a tuple (756) may be carried out by storing a set of data that indicates a rule identification and a rule state.

The method of FIG. 7 also includes the incident analyzer (700) identifying (708) from the generated tuples (756), tuples (758) that have the same rule identification (774). Identifying (708) from the generated tuples (756), tuples (758) that have the same rule identification (774) may be carried out by comparing the rule identifications of each of the generated tuples and identifying grouping of tuples together based on a match of rule identifications.

The method of FIG. 7 also includes generating (710) a merged tuple (760) including merging the rule state of each of the identified tuples (758) that have the same rule identification (774). Generating (710) a merged tuple (760) including merging the rule state of each of the identified tuples (758) that have the same rule identification (774) may be carried out by merging the rule states of each of the tuples that have the same rule identification. Merging the rule states may include removing duplicate incidents to form one complete merged state (776) that is a listing of incidents applicable to the rule type. Alternatively, if the rule state is a representation that at least one incident has satisfied at least a portion of the rule, merging the rule states may include determining whether any of the rules states are true or false and then based on that determination, setting the merged rule state (776) to either true or false.

That is, by spreading the processing of incidents across the plurality of threads, the processing of incidents is performed in parallel to generate the merged tuple (760).

For further explanation, FIG. 8 sets forth a flow chart illustrating an additional method of parallel incident processing in a distributed processing system according to embodiments of the present invention. The method of FIG. 8 is similar to the method of FIG. 7 in that the method of FIG. 8 includes: identifying (702) a pool (750) of incidents (752); distributing (704) the incidents (752) across a plurality of threads (754) of the incident analyzer (700); generating (706) a tuple (756); identifying (708) from the generated tuples (756), tuples (758) that have the same rule identification (774); and generating (710) a merged tuple (760) including merging the rule state of each of the identified tuples (758) that have the same rule identification (774).

The method of FIG. 8 includes the incident analyzer (700) distributing (802) across the plurality of threads (754) of the incident analyzer (700), the merged tuple (760) for incident suppression. Distributing (802) across the plurality of threads (754) of the incident analyzer (700), the merged tuple (760) for incident suppression may be carried out by generating a notification representing the processing of one or more incidents, such as an event or other alert.

The method of FIG. 8 also includes the plurality of threads (754) suppressing (804) the merged tuple (760). Suppressing (804) the merged tuple (760) may be carried out by determining in dependence upon the attributes of an incident whether to delete, drop, or otherwise ignore one or more of the incidents and removing one or more incidents from the merged rule state (776). For example, a thread may apply to the rule state, incident suppression rules associated with the incident analysis rules and determine that one or more incidents are redundant, identical, or superseded by another incident.

The method of FIG. 8 also includes the incident analyzer (700) distributing (806) across the plurality of threads (754), the suppressed merged tuple (850) for execution of the rule (860) corresponding to the same rule identification (774). Distributing (806) across the plurality of threads (754), the suppressed merged tuple (850) for execution of the rule (860) corresponding to the same rule identification (774) may be carried out by providing the suppressed merged tuple to each thread.

The method of FIG. 8 also includes at least one thread of the plurality of threads (754) generating (808) one or more alerts (854) based on the execution of the rule (860). Generating (808) one or more alerts (854) based on the execution of the rule (860) may be carried out by generating a representation of a processing of one or more events or alerts.

As explained above, an incident may be either an event or alert. That is, the methods of FIGS. 7 and 8 may be applied to process events or alerts. In addition, the methods of Figure and 8 may be applied to process events and then applied again to process the alerts generated from those events. Furthermore, the methods of FIGS. 7 and 8 may be applied iteratively to perform multiple rounds of incident processing, such as for example, a second or third round of incident processing of incidents from an incident pool.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims. 

What is claimed is:
 1. A method of parallel incident processing in a distributed processing system, the method comprising: identifying, by an incident analyzer, a pool of incidents; distributing, by the incident analyzer, the incidents across a plurality of threads of the incident analyzer, each thread having a set of incident processing rules; generating by one or more threads of the plurality of threads of the incident analyzer, a tuple, each tuple indicating a rule identification and a rule state; identifying from the generated tuples, by the incident analyzer, tuples that have the same rule identification; and generating, by the incident analyzer, a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.
 2. The method of claim 1 further comprising: distributing, by the incident analyzer, across the plurality of threads of the incident analyzer, the merged tuple for incident suppression; suppressing, by the plurality of threads of the incident analyzer, the merged tuple; distributing, by the incident analyzer, across the plurality of threads, the suppressed merged tuple for execution of the rule, the rule corresponding to the same rule identification; and generating by at least one thread of the plurality of threads of the incident analyzer, one or more alerts based on the execution of the rule.
 3. The method of claim 1 wherein the rule state is a collection of incidents.
 4. The method of claim 1 wherein the rule state represents that at least one incident has satisfied at least a portion of the rule.
 5. The method of claim 1 wherein the rule identification includes a location identification.
 6. The method of claim 1 wherein the rule identification includes a pool identification.
 7. The method of claim 1 wherein the incidents are alerts.
 8. The method of claim 1 wherein the incidents are events.
 9. An apparatus for parallel incident processing in a distributed processing system, the system comprising a computer processor and a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that when executed by the computer processor cause the apparatus to carry out the steps of: identifying, by an incident analyzer, a pool of incidents; distributing, by the incident analyzer, the incidents across a plurality of threads of the incident analyzer, each thread having a set of incident processing rules; generating by one or more threads of the plurality of threads of the incident analyzer, a tuple, each tuple indicating a rule identification and a rule state; identifying from the generated tuples, by the incident analyzer, tuples that have the same rule identification; and generating, by the incident analyzer, a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.
 10. The apparatus of claim 9 wherein the computer memory has disposed within it computer program instructions that when executed by the computer processor cause the apparatus to carry out the steps of: distributing, by the incident analyzer, across the plurality of threads of the incident analyzer, the merged tuple for incident suppression; suppressing, by the plurality of threads of the incident analyzer, the merged tuple; distributing, by the incident analyzer, across the plurality of threads, the suppressed merged tuple for execution of the rule, the rule corresponding to the same rule identification; and generating by at least one thread of the plurality of threads of the incident analyzer, one or more alerts based on the execution of the rule.
 11. The apparatus of claim 9 wherein the rule state is a collection of incidents.
 12. The apparatus of claim 9 wherein the rule state represents that at least one incident has satisfied at least a portion of the rule.
 13. The apparatus of claim 9 wherein the rule identification includes a location identification.
 14. The apparatus of claim 9 wherein the rule identification includes a pool identification.
 15. A computer program product for parallel incident processing in a distributed processing system, the computer program product disposed upon a computer readable storage medium, the computer program product comprising computer program instructions that when executed by a computer cause the computer to carry out the steps of: identifying, by an incident analyzer, a pool of incidents; distributing, by the incident analyzer, the incidents across a plurality of threads of the incident analyzer, each thread having a set of incident processing rules; generating by one or more threads of the plurality of threads of the incident analyzer, a tuple, each tuple indicating a rule identification and a rule state; identifying from the generated tuples, by the incident analyzer, tuples that have the same rule identification; and generating, by the incident analyzer, a merged tuple including merging the rule state of each of the identified tuples that have the same rule identification.
 16. The computer program product of claim 15 further comprising computer program instructions that when executed by a computer cause the computer to carry out the steps of: distributing, by the incident analyzer, across the plurality of threads of the incident analyzer, the merged tuple for incident suppression; suppressing, by the plurality of threads of the incident analyzer, the merged tuple; distributing, by the incident analyzer, across the plurality of threads, the suppressed merged tuple for execution of the rule, the rule corresponding to the same rule identification; and generating by at least one thread of the plurality of threads of the incident analyzer, one or more alerts based on the execution of the rule.
 17. The computer program product of claim 15 wherein the rule state is a collection of incidents.
 18. The computer program product of claim 15 wherein the rule state represents that at least one incident has satisfied at least a portion of the rule.
 19. The computer program product of claim 15 wherein the rule identification includes a location identification.
 20. The computer program product of claim 15 wherein the rule identification includes a pool identification. 